Created a separate file for Leaflet Maps becasue R crashed everytime we ran the document all in one (too much RAM?)
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## group_rows## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.1'
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## Use `force = TRUE` to force installation## Rows: 22 Columns: 16## ── Column specification ────────────────────────────────────────────────────────
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## chr (8): District_Name, Latino%, White%, Black%, Native_American%, Asian%, O...
## dbl (2): District_No, Native_American##
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## chr (9): LAST_NME, FIRST_NME, EMPLOYEE_POSITION, CPD_UNIT_ASSIGNED_NO, UNITA...
## dbl (2): AGE, STAR_NO##
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## chr (12): gender, race, current_rank, complaint_category, recommended_findi...
## dbl (7): row_id, cr_id, birth_year, current_unit, current_star, recommende...
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## date (1): appointed_date##
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## chr (6): beat, location_code, address_number, street, apartment_number, cit...
## dbl (2): row_id, cr_id
## date (3): incident_date, complaint_date, closed_date
## time (1): incident_time##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.district_complaints <- complaints_accused %>%
filter(current_unit %in% 1:25) %>%
group_by(current_unit) %>%
summarise(n = n()) %>%
arrange(desc(n))
total_district_complaints <- full_join(district_complaints,
district_demographics,
by = c("current_unit" = "District_No")) %>%
mutate(complaints_per_capita = n/Population)
#no 21 or 23 district but 31st district included?
chicago_police_district_spatial <- st_read(dsn = "/cloud/project/data/geo_export_2efb16ec-aa66-49b0-92a0-2d6f5e0f81d9.shp")## Reading layer `geo_export_2efb16ec-aa66-49b0-92a0-2d6f5e0f81d9' from data source `/cloud/project/data/geo_export_2efb16ec-aa66-49b0-92a0-2d6f5e0f81d9.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 25 features and 2 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -87.94011 ymin: 41.64455 xmax: -87.52414 ymax: 42.02303
## Geodetic CRS: WGS84(DD)#Removing % symbol, so the data can join
total_district_complaints_spatial <- total_district_complaints %>%
mutate(`Latino%` = str_remove(`Latino%`, "%"),
`White%` = str_remove(`White%`, "%"),
`Black%` = str_remove(`Black%`, "%"),
`Asian%` = str_remove(`Asian%`, "%"),
`Native_American%` = str_remove(`Native_American%`, "%"),
`Other%` = str_remove(`Other%`, "%"),
`Latino%` = as.numeric(`Latino%`),
`White%` = as.numeric(`White%`),
`Black%` = as.numeric(`Black%`),
`Asian%` = as.numeric(`Asian%`),
`Native_American%` = as.numeric(`Native_American%`),
`Other%` = as.numeric(`Other%`)) %>%
mutate(current_unit = as.character(current_unit))%>%
left_join(chicago_police_district_spatial,
by = c("current_unit" = "dist_num")) %>%
st_as_sf() %>%
st_transform("+init=epsg:4326")## Warning in CPL_crs_from_input(x): GDAL Message 1: +init=epsg:XXXX syntax is
## deprecated. It might return a CRS with a non-EPSG compliant axis order.#creating bins for our leaflet map
bins <- seq(from = 0, to = 100, by = 12.5)
pal_perc <- colorBin("OrRd", domain = total_district_complaints_spatial , bins = bins)
#https://laurielbaker.github.io/DSCA_leaflet_mapping_in_r/slides/leaflet_slides3.html#58
m <- leaflet(total_district_complaints_spatial) %>%
# Now add tiles to it
addTiles() %>%
# Setting the middle of where the map should be and the zoom level
setView(-87.633506, 41.876067, zoom = 9.5) %>%
addProviderTiles(providers$CartoDB.Positron)#map of distribution of black residents
Black_perc_m <- m %>%
addPolygons(
fillOpacity = 1,
color = "black",
opacity = 0.7,
weight = 1,
fillColor = ~pal_perc(total_district_complaints_spatial$`Black%`))
Black_perc_m <- Black_perc_m %>%
addLegend(
position = "topright",
pal = pal_perc,
values = ~total_district_complaints_spatial$`Black%`,
title = "Percent Black residents",
opacity = 1)
Black_perc_m#map of distribution of white residents
White_perc_m <- m %>%
addPolygons(
fillOpacity = 1,
color = "black",
opacity = 0.7,
weight = 1,
fillColor = ~pal_perc(total_district_complaints_spatial$`White%`))
White_perc_m <- White_perc_m %>%
addLegend(
position = "topright",
pal = pal_perc,
values = ~total_district_complaints_spatial$`White%`,
title = "Percent White residents",
opacity = 1)
White_perc_m#map of distribution of latino residents
Latino_perc_m <- m %>%
addPolygons(
fillOpacity = 1,
color = "black",
opacity = 0.7,
weight = 1,
fillColor = ~pal_perc(total_district_complaints_spatial$`Latino%`))
Latino_perc_m <- Latino_perc_m %>%
addLegend(
position = "topright",
pal = pal_perc,
values = ~total_district_complaints_spatial$`Latino%`,
title = "Percent Latino residents",
opacity = 1)
Latino_perc_m# creating map showing neighborhoods with most missing data ie when the final finding is either NA (missing), NAF (no affidavit) or NC (no affidavit)
#baseline complaints per capita
bins_2 <- seq(from = 0, to = 0.06, by = 0.01)
pal_per_cap <- colorBin("OrRd", domain = total_district_complaints_spatial, bins = bins_2)
complaints_perc_m <- m %>%
addPolygons(
fillOpacity = 1,
color = "black",
opacity = 0.7,
weight = 1,
fillColor = ~pal_per_cap(total_district_complaints_spatial$`complaints_per_capita`))
complaints_perc_m <- complaints_perc_m %>%
addLegend(
position = "topright",
pal = pal_per_cap,
values = ~total_district_complaints_spatial$`complaints_per_capita`,
title = "Complaints per capita",
opacity = 1)
complaints_perc_m